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Job ID: 128973

A fully funded PhD position in Deep learning for end-to-end motion planning of unmanned aerial vehic
Aarhus University

Date Posted Sep. 11, 2019
Title A fully funded PhD position in Deep learning for end-to-end motion planning of unmanned aerial vehic
University Aarhus University
Aarhus, Denmark
Department Department of Engineering
Application Deadline Nov. 1, 2019
Position Start Date Feb. 1, 2020
  • Graduate Student
  • Robotics
    Electrical and/or Electronics
    Computer Engineering
    Computer Science

We invite applications for a fully funded doctoral researcher position in the field of deep learning for end-to-end motion planning of unmanned aerial vehicles.

The project is supported by the H2020 ICTRIA program OpenDR for research and development in Deep Learning for Robotics.

In this project, we will introduce end-to-end motion planning methods for UAV navigation. Informed by a rough path to goal in partially known environments, the developed method will create desirable, local motion plans using raw images from the front-facing camera on quadrotor. According to our scenario, environment is partially known without exact obstacle location information, an initial rough path to goal is given, and concatenation of desirable local motion plans for safe navigation is to be found. Such scenarios can be seen in many indoor navigation problems, such as autonomous drone racing.

What you stand to gain: a fully funded PhD position for 3 years (starting February 2020) at the Department of Engineering, Aarhus University; a fun environment to drive your passion for robotics.

The research will be carried out under the supervision of Assoc. Prof. Erdal Kayacan ( at Artificial Intelligence in Robotics (Air) Lab:….

Qualifications and specific competences:


A Master’s degree in mechanical engineering, electrical engineering, aerospace engineering, computer science/engineering, control theory, mechatronics, applied mathematics, or other related disciplines,

Excellent verbal and writing skills in English with very good communication skills,

Experience in Robot Operating System (ROS), and

Concrete knowledge in C/C++.


Hands on experience in UAVs and basic understanding of UAV models,

Experience in machine learning methods; e.g. deep learning, and

Demonstration of research activities (conference or journal papers).


Applicants seeking further information are invited to contact:Assoc. Prof. Erdal Kayacan (

How to apply: Please follow the instructions here:…

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